Applied Data Scientist

Job description

causaLens is the pioneer of Causal AI — a giant leap in machine intelligence.

We are on a mission to build truly intelligent machines - it’s hard but super fun! If you want to build the future and are looking for a place that values your curiosity and ambition, causaLens is the right place for you. Everything we do is at the forefront of technological advancements and we are always on the lookout for people to join us whose skills and passion tower above the rest.

Since the company was established in 2017, causaLens has:
🥳Launched decisionOS, the first and only enterprise decision making platform powered by Causal AI - here

🦄Raised $45 million in Series A funding
🏆Named a leading provider of Causal AI solutions by Gartner - here
🚀Included in Otta’s 2022 Rocket List as one of the fastest-growing companies to launch your career

Our Mission

To radically advance human decision-making.

Our Vision

A world in which humans leverage trustworthy AI to solve the greatest challenges in the economy, society and healthcare.

Head to our website homepage and watch the ‘Why Causal AI’ video to learn more.

The Role

We are looking for an Applied Data Scientist based in London to join us in building our Causal AI technology to optimise every business on the planet. This is a full-time placement with significant opportunities for personal development. The Applied Data Scientist will be working on development of causal AI driven models and decision applications using our technology to directly impact client business needs.

What you will do

The Applied Data Scientist will work alongside our team of world class researchers, engineers and executives to help us become one of the most recognized names in Tech. You will be required to work directly with our customers, helping them to use our technology to solve their most pressing business needs. Some of your responsibilities will include:

  • Using our causal AI framework to help customers build causal models and Decision Applications, using our proprietary causal discovery, modelling and decision intelligence architectures.
  • Work directly with business stakeholders on the customer side to integrate domain knowledge into the modelling process and help them integrate insights into their decision workflows.
  • Work closely with the Product and Research teams to shape the development of our platform.

Job requirements

  • Please note that we are not looking for you to "tick all the boxes"
  • A strong command of Python (knowledge of R is a plus, but not a substitute)
    • Including end-to-end ML-driven solution development
  • A deep experience implementing solutions using regression, time-series
  • At least 2 years commercial data science experience
  • A strong command of probability and statistics major concepts, tests
    • A big bonus - if you had a chance to work with causal inference
  • Experience with productionising data science solutions is a plus
  • Experience building and deploying ML-driven solutions in AWS, GCP, Azure is a plus
  • Any items from the following list would be a bonus (the more the better, focusing on real-life implementation experience): all the classical ML algorithms and data science relevant Python libs, NLP, NN, DL, CV
  • Any items from the following list for be an extra bonus as well: TDD, Git/Hub/Actions/flow, Kedro-or-similar, MLflow, Databricks, Spark, C/C++/Java
  • Strong academic record in a quantitative field (MEng, MSci, EngD or PhD)
  • Excellent and proven communication and teamwork skills
  • Previous experience in high growth technology companies or technical consultancy is a plus
  • Experience in supply chain, demand forecasting, retail/cpg, manufacturing, financial services or public sector is a plus

About causaLens
Current machine learning approaches have severe limitations when applied to real-world business problems and fail to unlock the true potential of AI for the enterprise. causaLens is pioneering Causal AI, a new category of intelligent machines that understand cause and effect - a major step towards true artificial intelligence. Our enterprise platform goes beyond predictions and provides causal insights and suggested actions that directly improve business outcomes for leading businesses in asset management, banking, insurance, logistics, retail, utilities, energy, telecommunications and many others.

We may be biased but we believe you’ll be in good company. We offer a hybrid working set up and are dedicated to building an inclusive culture where diverse people and perspectives are welcomed. Aside from joining a smart and inspiring team, you’ll be amongst people who are always there to support your ideas and encourage you to grow. We celebrate our differences and come together to share our triumphs!

causaLens in the news

  • causaLens raises $45m Series A to scale Causal AI - Tech Crunch

  • Best Deeptech Company 2019 - Artificial Intelligence Awards

  • ‘Meet causaLens, a Predictive AI For Hedge Funds, Banks, Tech Companies’ – Yahoo Finance

  • ‘The U.K.’s Most Exciting AI Startups Race To Scale’ - Forbes

  • AllianzGI Taps Virtual Data Scientists amid War for Talent’ - Financial Times

  • ‘Machine Learning Companies to watch in Europe’ - Forbes

  • 'causaLens Appoints Hedge Fund Veteran and Data Leaders to Advisory Board’ - Newswire

  • Best Investment in Deeptech’ award - UK Business Angels Association Awards

  • ‘100 Most Disruptive UK Companies’ - Hotwire

What we offer
We care about our people’s lives both inside and outside of causaLens. Beyond the core benefits like competitive remuneration, pension scheme, paid holiday and a good work-life balance, we offer the following:

  • Access to mental health support through Spill
  • Competitive salary
  • Annual Discretionary Bonus
  • 25 days paid holiday plus bank holidays
  • Share options
  • Pension scheme
  • Happy hours and team outings
  • Referral bonus program
  • Cycle to work scheme
  • Friendly tech purchases
  • Office snacks and drinks


Our interview process consists of a few screening interviews and a "Day 0" which is spent with the team (either in the office or virtually, whatever you feel comfortable with). We will always be as transparent as possible so please don’t hesitate to reach out if you have any questions.